What Is a Data Historian?

Operational historian vs. enterprise historian

An operational historian sits close to the plant floor. It stores high-frequency raw data from SCADA systems and control equipment, with write speed and determinism as the primary design constraints.

An enterprise historian aggregates data from multiple operational historians across sites into a centralized store. It serves cross-site benchmarking, KPI reporting, and long-term trend analysis rather than real-time plant-floor monitoring.

On-premise vs. cloud historian

Traditional historian deployments are on-premise, running in the plant or data center alongside the control systems they monitor. A cloud historian moves this capability to cloud infrastructure, supporting multi-site consolidation, AI integration, and remote access without on-premise hardware.

CrateDB supports both models. Deploy at the edge for low-latency operational data capture, or in the cloud for consolidated analytics across sites. CrateDB Cloud is a fully managed option for teams that want historian-grade ingestion without infrastructure overhead.

The two categories overlap but are not the same.

Traditional historians were built for OT environments where stability and determinism matter more than query flexibility. Time series databases were built for IT environments where scale and developer access matter more than plant-floor integration.

CrateDB covers both: native OPC connectivity via Crosser, historian-grade ingestion rates, and standard SQL that data engineers can query without a proprietary interface. For the full comparison, see Data Historian vs. Time Series Database.

The historian market is dominated by a small set of long-established vendors.

• AVEVA PI System (formerly OSIsoft PI) is the most widely deployed historian. It uses a proprietary tag-based architecture and is standard in energy, utilities, and large-scale manufacturing.

• AspenTech IP.21 (InfoPlus.21) is standard in oil and gas, chemicals, and process manufacturing. It offers deep process analytics within its original deployment model.

• AVEVA Historian (formerly Wonderware Historian) is common in discrete manufacturing and factory automation, typically deployed alongside AVEVA InTouch SCADA.

• Canary Historian is a more recent option with cloud capabilities, used across mid-market industrial deployments.

Each product does what it was designed for. None was designed for multi-model queries, SQL access from data science teams, or horizontal scaling beyond a single site. That is the gap CrateDB addresses. CrateDB provides a documented modernization path from AVEVA PI with schema migration guidance and connector support.

Legacy historians often struggle when industrial data strategies mature. Common challenges include:

• Proprietary storage formats
• Limited scalability for high-resolution data
• Difficult cloud/edge integration
• Lack of flexibility for semi-structured or unstructured data
• Slow access to granular historical records
• High licensing and expansion costs
• Limited support for advanced analytics or AI workloads

Modern Industry 4.0 and digital transformation programs require a historian that works across OT and IT, natively supports cloud and edge deployments, and integrates easily with AI/ML pipelines. 

CrateDB is designed for the next generation of industrial data workloads. It combines time-series performance with multi-model flexibility, delivering ingestion, storage, and analytics in one distributed SQL engine. Key capabilities are:

• High-frequency ingestion: Capture data from sensors, equipment, logs, and control systems at high throughput.

• Instant query availability: Automatic indexing ensures that new data is ready for analytics within milliseconds.

• Support for all industrial data types: Store time-series, relational, JSON, logs, text, geospatial, vector, and BLOB data in the same engine.

• Flexible SQL interface: Use standard SQL without proprietary query languages or tools.

• Fast analytics on large datasets: Run queries, aggregations, joins, search, and AI workloads on years of high-resolution data.

• Horizontal scalability: Add nodes to increase capacity and maintain performance as data volumes grow.

• Edge, on-premise, hybrid or cloud deployment: Same engine, full performance, in any environment.

• AI-ready architecture: Feed high-quality historical and real-time data directly into AI and predictive maintenance models.

Modern industrial architectures require solutions that work seamlessly across both OT and IT domains. CrateDB provides a unified data foundation that:

• Connects to OT systems on the shop floor
• Integrates with IT systems in the cloud and data center
• Handles structured and unstructured data from both sides
• Supports AI, ML, BI, and enterprise analytics
• Delivers consistent performance from edge to cloud

CrateDB integrates with Crosser, a hybrid-first streaming and integration platform designed for industrial environments.

Crosser enables:

• Direct connectivity to PLCs, SCADA, MES, sensors, and equipment
• Support for industrial protocols (OPC-UA, Modbus, MQTT, etc.)
• Edge nodes that run on-site behind the firewall
• Low-code data flows for filtering, enrichment, transformation
• Real-time stream analytics at the edge
• OT-to-IT data pipelines delivering cleaned operational data into CrateDB

For organizations that need contextualized, governed, AI-ready operational data, CrateDB partners with EOT.AI.

EOT.AI provides:

• No-code/low-code pipelines that extract data from SCADA, historians, legacy OT systems
• A semantic layer that models assets, hierarchies, and metadata
• Unification of operational data with business and contextual data
• Data governance, quality, lineage, and access control
• AI-ready data products combining time-series, events, metadata, and context
• Integration with cloud analytics platforms, BI tools, and ML pipelines

CrateDB integrates ingestion, storage, and analytics into a fault-tolerant distributed architecture.

Ingestion:

• MQTT, Kafka, Flink, REST, IoT gateways, batch imports
• OPC-UA, Modbus, SCADA, MES, PLCs (via Crosser or EOT.AI)
• High-throughput, low-latency pipelines
• Automatic indexing and dynamic schemas

Storage

• Optimized for time-series
• Up to 70% data compression
• Handles structured, semi-structured, and unstructured data
• Automatic sharding and replication

• Sub-second queries on large time-series datasets
• Aggregations, downsampling, trend analysis
• Vector search for anomaly detection and AI
• Text search for logs and contextual data
• Real-time dashboards, monitoring, and alerting tools

Many industrial teams are moving toward a Unified Namespace (UNS) architecture, where all OT and IT data flows through a central message broker before reaching the storage and analytics layer. CrateDB integrates directly into UNS architectures as the operational database below the broker, keeping every sensor reading queryable the moment it arrives.

CrateDB fits naturally into industrial architectures. It connects with:

• SCADA / MES / PLCs (via Crosser or EOT.AI)
• Industrial gateways (via Crosser or EOT.AI)
• Cloud IoT hubs
• BI tools
• AI/ML platforms

It can serve as:

• A primary historian
• A scalable extension to an existing historian
• A unified data hub combining historian, IoT, and contextual IT data
• A foundation for AI and predictive analytics initiatives

Many industrial organizations rely on the AVEVA PI System for operational data collection. As deployments grow, teams often encounter limits around scale, proprietary formats, analytics flexibility, cloud integration, or total cost of ownership.

CrateDB offers a modern path forward: it can replace traditional historians such as AVEVA PI when organizations need a scalable, open, and cost-efficient platform for high-resolution time-series data, advanced analytics, and AI workloads. Many companies keep their existing OT data collection layer (PLCs, SCADA, OPC connectors, or PI interfaces) while adopting CrateDB as the new long-term historian and analytics backbone.

CrateDB is especially effective in PI modernization scenarios where teams want to:

• Move beyond storage bottlenecks
• Retain high-resolution data cost-effectively
• Run advanced SQL analytics across operational and contextual data
• Power AI and predictive maintenance models
• Integrate historian data with cloud, BI, and data platforms
• Consolidate data silos into a unified storage and analytics engine
• Support hybrid edge-to-cloud architectures

A dedicated historian is the right choice if your operation meets any of these conditions:

• Your equipment communicates via OPC-UA, OPC-DA, or Modbus and requires a collector that speaks those protocols natively
• You need to store sensor readings at millisecond or sub-second frequency across hundreds or thousands of tags
• Your regulatory environment (FDA 21 CFR Part 11, ISO 50001, energy reporting) requires deterministic data capture with quality codes
• You already run a PI System or AVEVA Historian and want to extend its analytics layer without replacing it

Consider CrateDB as your historian layer if you need to go beyond these requirements: querying raw records with standard SQL, combining sensor data with JSON event logs or vector embeddings, scaling across sites without vertical hardware limits, or feeding operational data directly into AI pipelines.

CrateDB runs everywhere with the same reliability and performance:

• On-premise for regulated environments
• Edge for real-time local processing
• Cloud for elastic scalability
• Hybrid deployments combining any of the above

• Real-time visibility into operations
• High-resolution storage at lower cost
• Unified OT + IT data foundation
• Semantic modeling and AI readiness via EOT.AI
• Reliable OT connectivity via Crosser
• Faster analytics without ETL
• Open SQL interface
• Ideal for predictive maintenance and digital twins